1. Introduction

Asian dust refers to the small sand and dust particles drifting in the air and travelling long distances with the upper winds from arid regions in northern China and Mongolia before gradually falling back on to land. Such large-scale movement of clouds of sand primarily influences the eastern Asia regions, but they can travel as far as the western regions of the U.S. and Canada (McKendry et al., 2001). The Asian dust phenomenon has been recorded in a number of historical records of Korea since the second century (Chun et al., 2008), and Asian dust storms (ADSs) lasting over 10 days have occurred every year since 2000, except in 2003 and 2004 (Korea Meteorological Administration (KMA), 2010).

ADS events are most commonly observed in Korea and Japan under a high pressure system in association with strong surface winds, which are often excited by the passage of cold fronts, and baroclinic instability at the 1.5 km level (Hayasaki et al., 2006; Yasunari and Yamazaki, 2009). Chun et al. (2001) showed that meteorological conditions for ADSs, mainly characterized by periodical alternation between synoptic scale high and low pressure systems, frequently occurs in spring in East Asia. Climatologically, the Gobi Desert of Mongolia and the Taklamakan Desert of northwestern China, known as major sources of ADS, provide a favourable environments for the occurrences of ADS events and subsequent dust transport across East Asia (Yu et al., 2010). The 3 day back trajectories of ADSs arriving at Seoul on an ADS day (30 May 2008) are shown in Figure 1, demonstrating that the ADS event originated from Mongolia and China and crossed the Yellow Sea (Jeon et al., 2011).

Figure 1.

With increasing concerns regarding the influence of Asian dust on human health, there have recently been a large number of studies performed on this issue. According to Donaldson and MacNee (1998), the effects of particulate matters on health are mainly related to the size and chemical composition of the particle. According to the findings of several studies, there is a dramatic increase in the concentration of coarse particles (between 2.5 and 10 µm in diameter) compared to fine particles (aerodynamic diameters equal to or less than 2.5 µm) on the days of ADS events. This is associated with high wind speed, which plays a role in decreasing the concentration of fine particles and other combustion-related pollutants. Thus, the PM10 levels increase greatly above the normal levels caused by the local conditions (Husar et al., 2001). Studies considering the nature of Asian dust have investigated the effects of ADSs on mortality (Kwon et al., 2002; Chen et al., 2004; Lee et al., 2007), respiratory symptoms (Hwang et al., 2003; Lei et al., 2004; Park et al., 2005; Hong et al., 2010), cardiovascular diseases (Meng and Lu, 2007) and stroke (Yang et al., 2005). Studies performed to date have analysed the frequencies of diseases and deaths arising from the occurrence of ADSs, but there have not been any studies analysing the frequency of onset of diseases caused by the variation in the PM10 concentration. Thus, the purpose of this study is to not only analyse the effects of ADS itself but also the effects of the PM10 concentrations on the days of the ADS event on the occurrence of asthma, a respiratory disease, among the residents of Seoul from 2005 to 2008. Also, the analysis of the PM10 concentrations was performed by grouping the 24 h mean concentrations used by the majority of previous researches separately from the 8 h mean concentrations between the hours of 1000–1800 LST (LST = UTC + 7 h) during which people are actually active outdoors.

2. Material and methods

2.1. Area of study and data

This study focused on the specific region of Seoul, the capital city of South Korea, which is located in the centre of the Korean peninsula and has a temperate continental climate with a warm summer. Seoul has a population of approximately 10.5 million and an area of 605 km2. The population of Seoul accounts for one-fifth of the total population of South Korea, while the area of Seoul is equal to 0.6% of South Korean territory.

The data on the number of medical services provided to asthma patients at all hospitals and clinics in the Seoul region from 2005 to 2008 were obtained from the Health Insurance Review and Assessment Service (HIRAS). The HIRAS, a health care organization affiliated with the government, is primarily responsible for reviewing medical bills and has a computerized record of the daily medical records from all hospitals and clinics. The data associated with the air pollution levels in the same periods and regions as the medical data were provided by the National Institute of Environmental Research. The data consisted of the hourly measurements of the carbon monoxide, nitrogen dioxide, sulphur dioxide, ozone and PM10 concentrations, which were compiled from 27 monitoring stations distributed evenly throughout Seoul.

The hourly measurements of the five air pollutants were converted into 24 h and 8 h (1000–1800) mean concentrations, which were used as the basic materials of the analysis. In addition, the information associated with the occurrence of ADSs in the same periods and regions was obtained from the Korea Meteorological Administration. According to the data, the ADS phenomenon was observed in the Seoul region for a total of 40 days from 2005 to 2008 (Table 1).

2.2. Analysis method

In general, studies conducted to investigate the relationship between air pollution and diseases must have a control for patterns of the day of the week, seasons and human behaviour according to the purpose of the study. In order to control confounding by long-term time series patterns, day of the week and seasonal trends, this study employed the concept of index days and symmetrical comparison days (Bateson and Schwartz, 1999; Chen et al., 2004; Yang et al., 2005). Index days correspond to the days when the ADS events actually occurred, while 7 days before and after the ADS occurrence day (the index day) were designated as comparison days.

In addition, in cases of two or more consecutive days of ADS, the harvesting effect was taken into consideration; individuals who are vulnerable to diseases brought on by ADS will visit the hospital on one of the consecutive ADS days and, thus, there is a decrease in the number of visits to the hospital on the following days. This phenomenon may decrease the accuracy of the estimation and analysis of the correlation between ADSs and related diseases (Yang et al., 2005). Thus, for cases where an ADS occurred for two or more consecutive days, only the last day of such an event was considered as the index day. However, if the difference between the PM10 concentrations of the days in question was over 110 µg m−3, all the days of the ADS period were considered as the index days for analysis. For instance, the 8 h mean PM10 concentrations on 1 April 2007 and the following day are 583.89 and 59.57 µg m−3, respectively, and thus have a difference of over 110 µg m−3. Both days were designated as index days in spite of the fact that they are consecutive days of ADS events. The 8 and 9 May 2007 and 30 and 31 May 2008, during which there was a continuous occurrence of ADSs, were also designated as the index days for the same reason.

Paired t-tests were carried out on the mean numbers of medical services provided to asthma patients on the index and comparison days for analysis. If an ADS occurred on one of the two comparison days only the number of medical services provided on the other comparison day was used as data for the paired t-test. If an ADS occurred on both comparison days of an index day, they were excluded from the analysis data. Thus, 3 days from 20 to 22 April 2005 satisfying this criterion were excluded from the data. The index days and comparison days selected according to these criteria are shown in Table 2.

Table 2. Asian dust storm days selected for the analysis in this study

Year

Date

24 h PM10 (µg m−3)

8 h PM10 (µg m−3)

Mean

Max

Mean

Max

8 h based PM10 concentration levels were obtained in the period 1000–1800.

2005

29 March

103.1

234

120.3

223

7 April

109.0

197

158.5

197

10 April

74.0

252

41.6

112

15 April

89.8

136

104.7

118

29 April

118.8

165

115.4

147

7 November

90.6

150

66.9

96

2006

11 March

173.2

337

242.3

337

13 March

131.4

326

183.9

323

28 March

94.2

168

110.0

168

9 April

344.2

1080

195.3

274

18 April

102.7

144

95.4

135

24 April

177.3

257

226.1

257

1 May

150.2

240

122.3

189

2007

14 February

80.3

152

119.6

152

6 March

98.2

206

105.4

200

28 March

175.3

259

205.8

259

1–2 April

380.0

851

583.9

851

8–9 May

111.6

336

220.0

336

26 May

220.2

387

149.3

238

29 December

252.0

425

378.0

425

2008

12 February

135.3

193

158.3

193

3 March

63.9

98

74.4

98

16 March

120.3

173

145.7

171

4 April

97.9

136

77.3

118

30–31 May

261.0

687

429.0

687

The effects of ADSs on human health tend to appear days later and asthma is not a type of disease that must be treated immediately upon onset, such as stroke or heart diseases. Therefore, a patient's visit to the hospital or clinic may take place a few days after the ADS event. In order to reflect this phenomenon, a moving mean of the number of daily medical services provided from the day of ADS occurrence to the corresponding lag day was used in the paired t-test. For example, in order to observe the lag effect between the day of ADS occurrence and 3 days after the occurrence, a 4 day moving mean of the number of medical services provided on the day of ADS occurrence, i.e., the index day, and the next 3 days as well as a 4 day moving mean of the number of medical services provided on the comparison day and the next 3 days were used in the paired t-test for analysis.

Lastly, in order to investigate the effects of the intensity of Asian dust, i.e., the PM10 concentration, the index days were categorized into a group with high mean PM10 concentrations and a group with low mean PM10 concentrations. The groups were classified based on whether their PM10 concentration level was above or below the mean PM10 concentration of 110 µg m−3, which is twice the mean PM10 concentration level reported in the Seoul region during 2005–2008. The groups were separately determined for the cases of 24 h and 8 h, and a paired t-test was performed for each group.

3. Results and discussion

3.1. Comparison of air pollutants level

Table 3 shows the mean values and 95% confidence intervals of the concentrations of the air pollutants in the Seoul region on the index days and comparison days from 2005 to 2008. The summary statistics of the daily concentrations of the variables in Table 3 were obtained and compared separately for 24 h and 8 h. The results of the paired t-test showed that the mean PM10 and NO2 concentrations were statistically significant in the cases of 24 h and 8 h. In contrast, the mean concentrations of other air pollutants (CO, O3, SO2) showed slightly different results depending on whether the calculation of the mean values was based on a 24 h or 8 h period. The mean concentrations of CO and SO2 were higher on the comparison days than the index days and the 24 h mean concentration of CO and the 8 h mean concentration of SO2 were statistically significant. As for O3, the 24 h mean concentration was observed to be slightly higher on the index days than the comparison days but the 8 h mean concentrations of these days were similar.

Table 3. Daily means of environmental variables on Asian dust days (index days) and comparison days in Seoul, Korea, 2005–2008

24 h mean

8 h mean

Index days

Comparison days

p-value

Index days

Comparison days

p-value

PM10 (µg m−3)

143.38 ± 31.67

77.73 ± 8.85

0.0004

165.59 ± 47.13

76.14 ± 9.66

0.0006

SO2 (ppb)

5.67 ± 0.79

6.75 ± 0.82

0.0522

5.88 ± 0.77

7.46 ± 1.01

0.0254

O3 (ppb)

25.32 ± 4.28

21.34 ± 3.06

0.0612

31.33 ± 5.00

31.96 ± 4.46

0.7955

NO2 (ppb)

35.77 ± 3.75

47.72 ± 5.25

0.0022

31.83 ± 3.93

40.03 ± 4.91

0.0204

CO (ppm)

0.52 ± 0.07

0.66 ± 0.12

0.0322

0.48 ± 0.07

0.56 ± 0.09

0.1441

There have been numerous studies conducted to analyse the concentrations of air pollutants, including PM10, on ADS events (Kwon et al., 2002; Chen et al., 2004; Lei et al., 2004; Park et al., 2005; Yang et al., 2005; Lee et al., 2007). However, it is difficult to find research results that are consistent with each other when the PM concentration is excluded. The results of the analysis of air pollutant concentrations obtained in the present study demonstrate a similar pattern with the research findings of Lei et al. (2004) and Park et al. (2005): in particular, the SO2, NO2 and CO concentrations were found to be higher on the comparison days compared to the index days. This is thought to be caused by the effect of the high surface wind speed accompanying ADSs to reduce the level of fine particles and other combustion-related pollutants (Schwartz et al., 1999; Chun et al., 2001). The combustion-related pollutants (SO2, NO2, CO) and PM10 are known to have a significant association with asthma symptoms (Schwartz, 1995; Seaton et al., 1995; Tattersfield, 1996; WHO, 2005). Unlike PM10, however, the fact that their concentrations are lower on the index days compared to the comparison days may have a counter result in performing an analysis based on the PM10 concentration. However, because the concentrations of the air pollutants measured in this study do not exceed those of the WHO guidelines (WHO, 2005), the effects of SO2, NO2 and CO on asthma were not analysed additionally. The concentrations of O3 calculated based on a 24 h period and an 8 h period showed no statistically significant difference between the index days and the comparison days and, thus, O3 was not believed to be a factor in the increase of asthma patients on the day of ADS events and was excluded from the analysis.

3.2. Annual pattern of asthma disease

In Seoul, asthma disease occurred with a monthly clinical attack rate of 1.7% during the period from 2005 to 2008. The relationship between ADS and asthma attack rate can be clarified through the annual pattern of the occurrences of asthma disease and ADS shown in Figure 2. A great proportion of ADS during the 4 years, i.e. 30 out of 34, occurred in March to May. On the other hand, asthma attacks had two major peaks around March to May and November to December. A clear relationship between ADS and asthma attack can be seen from the peak around March to May, as the greater part of the ADS days in Table 2, which are used for this study, correspond to the peak. The other peak, around November to December, is known to be caused mainly by low temperature (Frei, 1995) and is not considered in this study.

Figure 2.

Pattern of ADS events and asthma attacks during the study period. Line graph shows the average number of asthma patients and bar chart indicates the total number of ADS events for each month during 2005–2008

3.3. Analysis results of the relationship between ADS event and asthma disease

Table 4 shows the mean values, 95% confidence intervals and the p-values obtained from the paired t-tests for the multiday moving mean values of the number of asthma patients' hospital or clinic visits on the index and comparison days. The percentage increase of medical services provided for asthma on the comparison days compared to the index days according to the day lags showed that there was a 3.79% increase in the number of medical services on the day of ADS occurrence (0 day lag) and that there was continuous increase along with the day lags with an increase of approximately 18% on days 4–6 after the ADS event.

Table 4. Number of asthma patients on Asian dust days and comparison days by various lags

(a)

The same day

1 day lag

2 day lag

Index days

6837.89 ± 2348.43

6434.77 ± 1714.96

6755.37 ± 1457.30

Comparison days

6588.45 ± 1571.19

6136.97 ± 1027.33

6084.91 ± 708.31

% increase (%)

3.79

4.85

11.02

p-value

0.4078

0.3588

0.1692

(b)

3 day lag

4 day lag

5 day lag

6 day lag

6780.08 ± 1295.79

6578.37 ± 1176.76

6549.01 ± 1037.22

6596.93 ± 1037.22

5872.47 ± 676.92

5572.68 ± 578.27

5561.28 ± 540.83

5582.01 ± 540.83

15.46

18.05

17.76

18.18

0.0645

0.0322

0.0264

0.0152

The response lag time used to analyse the short-term effects of air pollutants on health is commonly less than 4 days (Katsouyanni et al., 1997; Chen et al., 2004; Kwon et al., 2002; Yang et al., 2005; Lee et al., 2007; Leung et al., 2008). However, Meng and Lu (2007) analysed their data by considering up to a 6 day lag and demonstrated that the effects of Asian dust on the symptoms of pneumonia are statistically significant even at 6 day lag. Because asthma also belongs to the category of respiratory diseases, just as pneumonia, data obtained up to the 6 day lag period were analysed. The mean number of daily medical services provided for asthma on the index days was higher than that of the comparison days, regardless of the day lags. There was a negative correlation between the day lags and the p-values, and the mean numbers of medical services in between the 4 day lag and 6 day lag were statistically significant at a significance level of 5%. Therefore, the mean daily number of patients who visited the hospital or clinic for asthma treatment 4, 5 or 6 days after the day of an ADS event, i.e., the index day, was higher than the mean daily number of asthma patients on the same day lags from the comparison days and this was statistically significant. This indicates that there is a clear lagged effect of the exposure 4 days after the index day, and it reflects the nature of asthma as it is different from the findings that there is the greatest lagged effect of the exposure on death at 2 day lag (Kwon et al., 2002; Chen et al., 2004) and that there is the greatest lagged effect of the exposure on stroke at 3 day lag. In other words, considering the seriousness of the diseases, stroke or death requires the case to be reported to a hospital or the patient to be taken to the hospital immediately and, thus, such cases are recorded immediately upon their occurrence. However, there may be a delay in the onset of asthma symptoms caused by Asian dust and patients may delay their visit to the hospital if their symptoms are not too serious depending on their personal situations. Thus, it could be said that the lag effect of the exposure on asthma may appear 1–2 days later compared to death or stroke due to its nature.

Table 5 shows the results of the comparative analysis of the numbers of medical services provided in the groups with PM10 concentration levels that fall below or above 110 µg m−3. When the PM10 concentration was calculated based on the 24 h mean value, which has been commonly used in previous studies, the numbers of medical services provided on all day lags were slightly higher in the group with PM10 > 110 µg m−3 compared to the group with PM10 < 110 µg m−3, but the difference was not significant. On the other hand, when the PM10 concentration was calculated based on an 8 h mean concentration measured from 1000 to 1800, during which there are frequent outdoor activities, the mean numbers of medical services on the day of an ADS and 1 day lag were slightly higher in the group with PM10 < 110 µg m−3 compared to the group with PM10 > 110 µg m−3. However, starting from the 2 day lag, the number of medical services increased in the group with PM10 > 110 µg m−3 and this trend became more noticeable as the day lags passed with a statistically significant difference shown at 6 day lag.

Table 5. Number of asthma patients on Asian dust days with PM10 > 110 µg m−3 and those with PM10 < 110 µg m−3 by various lags

(a)

The same day

1 day lag

2 day lag

24 h mean

PM10 > 110

7308.07 ± 3159.30

6949.57 ± 2205.56

7325.07 ± 1880.16

PM10 < 110

6367.71 ± 1397.48

5919.96 ± 1166.24

6185.67 ± 972.28

% increase (%)

14.77

17.39

18.42

p-value

0.3453

0.2748

0.2177

8 h mean

PM10 > 110

6704.11 ± 2767.49

6386.06 ± 1977.41

6834.13 ± 1718.72

PM10 < 110

7078.70 ± 1457.06

6495.65 ± 1224.96

6613.60 ± 899.53

% increase (%)

− 5.29

− 1.69

3.33

p-value

0.4284

0.4723

0.432

(b)

3 day lag

4 day lag

5 day lag

6 day lag

6988.25 ± 1598.30

7066.17 ± 1452.14

6865.11 ± 1197.49

6974.55 ± 1017.98

6571.91 ± 996.87

6090.57 ± 875.55

6232.90 ± 891.08

6219.31 ± 843.56

6.34

16.02

10.14

12.14

0.3745

0.2035

0.2712

0.2067

7007.58 ± 1512.12

6858.99 ± 1357.22

7008.93 ± 1209.05

7055.81 ± 1045.07

6370.58 ± 832.31

6073.26 ± 789.52

5721.15 ± 557.76

5770.94 ± 583.87

10.00

12.94

22.51

22.26

0.2906

0.2294

0.0736

0.0485

Table 6 shows the results of the paired t-test on the moving mean values of the number of asthma patients on the index and comparison days for each of the sub-groups, which were categorized based on whether their 24 and 8 h PM10 concentrations fell below or above 110 µg m−3. In the group with a 24 h mean PM10 < 110 µg m−3, the number of asthma patients on the index days was higher than the comparison days by as much as 12.67% (at 3 day lag). In the group with a 24 h mean PM10 > 110 µg m−3, the number of asthma patients on the index days was higher compared to the comparison days by as much as 28.27% (at 6 day lag), which was statistically significant. When the PM10 concentrations were calculated based on the 8 h mean, the increase in the number of asthma patients was highest at 1 day lag with a 24.83% increase in the group with PM10 < 110 µg m−3. In the group with an 8 h mean PM10 > 110 µg m−3, there were 23.30 and 26.85% increases at 5 and 6 day lags, respectively, which were statistically significant.

Table 6. Number of asthma patients on Asian dust days and comparison days in groups with PM10 > 110 µg m−3 and with PM10 < 110 µg m−3 by various lags

(a)

The same day

1 day lag

2 day lag

24 h PM10 < 110

Index days

6367.71 ± 2032.26

5919.96 ± 1708.15

6185.67 ± 1419.26

Comparison days

6760.14 ± 2127.06

6064.98 ± 1582.99

5955.83 ± 911.39

% increase (%)

− 5.81

− 2.39

3.86

p-value

0.6388

0.5599

0.3801

24 h PM10 > 110

Index days

7308.07 ± 4594.35

6949.57 ± 3230.39

7325.07 ± 2744.50

Comparison days

6416.75 ± 2611.95

6208.96 ± 1533.12

6213.99 ± 1211.50

% increase (%)

13.89

11.93

17.88

p-value

0.3191

0.2971

0.1818

8 h PM10 < 110

Index days

7078.70 ± 2683.21

6522.45 ± 2230.63

6613.60 ± 1656.52

Comparison days

5926.75 ± 2457.05

5225.25 ± 1392.88

5598.30 ± 930.60

% increase (%)

19.44

24.83

18.14

p-value

0.1577

0.0808

0.0988

8 h PM10 > 110

Index days

6704.11 ± 3542.83

6386.06 ± 2463.88

6834.13 ± 2200.23

Comparison days

6956.06 ± 2192.02

6643.49 ± 1439.61

6355.25 ± 1017.55

% increase (%)

− 3.62

− 3.87

7.54

p-value

0.4362

0.4145

0.3196

(b)

3 day lag

4 day lag

5 day lag

6 day lag

6571.91 ± 1468.59

6090.57 ± 1286.29

6232.90 ± 1319.08

6219.31 ± 1251.38

5832.68 ± 1044.89

5436.75 ± 824.06

5558.15 ± 830.53

5726.83 ± 716.56

12.67

12.03

12.14

8.60

0.1488

0.1492

0.1575

0.2040

6988.25 ± 2354.61

7066.17 ± 2133.39

6865.11 ± 1772.66

6974.55 ± 1510.12

5912.27 ± 1008.52

5708.60 ± 920.60

5564.40 ± 810.65

5437.19 ± 636.96

18.20

23.78

23.38

28.27

0.1419

0.0701

0.0522

0.0193

6370.58 ± 1532.72

6073.26 ± 1453.92

5721.15 ± 1025.14

5770.94 ± 1075.21

5483.20 ± 856.20

5266.18 ± 767.22

5339.83 ± 767.80

5617.63 ± 587.74

16.18

15.33

7.14

2.73

0.1159

0.1279

0.2756

0.3895

7007.58 ± 1935.75

6858.99 ± 1737.45

7008.93 ± 1544.77

7055.81 ± 1337.85

6088.74 ± 987.48

5742.95 ± 837.65

5684.31 ± 775.98

5562.22 ± 664.30

15.09

19.43

23.30

26.85

0.1418

0.0736

0.0333

0.0123

In cases of high PM10 concentration levels, the p-values of the paired t-test for each day lag show similar patterns of lag effect to those identified for all days of the ADS occurrence regardless of PM10 level. In other words, the lag effect of the exposure increased along with the day lag, and there was a significant correlation between the ADS events and the number of asthma patients with the 5 and 6 day lag effect. However, in cases of high PM10 level based on a 24 h period, the p-value at 5 day lag was 0.0522, which could be considered statistically significant despite the fact that it is higher than 0.05. On the other hand, when the PM10 levels were low, there were no day lags that showed a significant correlation. However, different patterns were shown depending on whether the mean PM10 concentration was calculated based on a 24 h period or an 8 h period. That is, when the 24 h mean PM10 level was low, the p-values for all day lags showed a pattern of being higher than the days when the PM10 levels were higher. However, when the PM10 level was calculated based on an 8 h period, the p-value patterns on the days with low PM10 levels showed an opposite pattern from the days with high PM10 levels, i.e. there was a decrease in the lag effect of the exposure as the lag days passed. To summarize, there was a clear lag effect and increase in the number of asthma patients on the days with high PM10 concentrations regardless of the time period the measurement was based on. The increase in the number of asthma patients on the days with low PM10 levels was minimal and it was determined that the pattern of the lag effect was different according to the time period the calculation of the mean PM10 level was based on. On days when the mean PM10 concentration based on the 8 h period, during which outdoor activities are most frequent, was below 110 µg m−3, there were patterns of a decrease in the lag effect and an increase in the number of asthma patients shortly after the occurrence of an ADS, even though they were not statistically significant. On the other hand, on the days of 24 h PM10 < 110 µg m−3 there was actually a decrease in the number of asthma patients immediately after the occurrence of an ADS. These patterns were analysed in detail in terms of the increase in the percentage of medical services on the index days compared to the comparison days, shown in Tables 4 and 6, and it was found that the percentage increase of the group with a 24 h PM10 < 110 µg m−3 was lower in all day lags compared to the total percentage increase (Table 4) and that the percentage increase of the group with a 24 h PM10 > 110 µg m−3 was higher in all day lags compared to the total percentage increase. In contrast, for the group with an 8 h PM10 < 110 µg m−3, the percentage increase in the medical services on the index days was higher than the total percentage increase up to a 3 day lag and was lower than the total percentage increase from 4 day lag and onwards, whereas the group with an 8 h PM10 > 110 µg m−3 showed the opposite pattern. To put it differently, when the 8 h mean PM10 concentration was low, there was a short-term lag effect up to 3 day lag, whereas when the 8 h mean PM10 concentration was high, there was a relatively longer lag effect with a delay of 4 or more days.

The p-values from Tables 4 and 6 are represented in the form of a graph for each case of the PM10 concentrations and day lags in Figure 3. In general, a p-value represents the extent to which the data disagree with the alternative hypothesis (H1), which is that the number of medical services provided for asthma on the index days is higher than that of the comparison days (Groebner et al., 2008). Thus, a small p-value indicates that there is actually an increase in the onset of asthma patients after ADS. A comparison of Figure 3(a) and (b) shows that two groups, of which 24 h and 8 h mean PM10 concentrations are under 110 µg m−3, have different patterns of lag effects.

Figure 3.

The p-values of paired t-test for groups with PM10 < 110 and with PM10 > 110 by various lags

4. Concluding remarks

This paper analysed the effect of Asian dust storm (ADS) events on asthma attacks by employing the concepts of lag effects and severity of the ADS (i.e., the degree of PM10 concentration level). Results showed that there is a strong association between ADS events and asthma patients for 4–6 days lag. Severe ADS events with mean PM10 > 110 µg m−3 have a more adverse effect on asthma attacks. However, in cases of low PM10 levels the percentage increase patterns in asthma treatment counts were completely different depending on the period that the mean PM10 concentration measurement was based on (i.e., 24 h or 8 h). In order to investigate the exact cause, behavioural studies on the psychology and life cycles of the general population must be conducted along with pathological analysis. Also, the results suggest that it is important to collect the data on the environmental variables after careful designation of the appropriate measurement period that meets the purpose of the analysis. In conclusion, this study can be extended to forecast asthma attacks, of which results can be used to organize medical resources and prevent the prevalence of asthma for medical service providers and the public.

Acknowledgements

The present research was conducted by the research fund of Dankook university in 2013.

Lee JT, Son JY, Cho YS. 2007. A comparison of mortality related to urban air particles between periods with Asian dust days and without Asian dust days in Seoul, Korea, 2000–2004. Environ. Res.105: 409–413.